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Statistical package for social science version

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Sourced in United States

The Statistical Package for Social Science (SPSS) is a software application used for statistical analysis and data management. It provides a comprehensive set of tools for data manipulation, exploration, and analysis. SPSS is widely used in various fields, including social sciences, market research, and healthcare, to help researchers and professionals analyze and interpret their data.

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Lab products found in correlation

9 protocols using statistical package for social science version

1

Fatty Acids Intake and Metabolic Syndrome

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The Statistical Package for Social Science (version 15.0; SPSS Inc, Chicago IL) was used for all statistical analyses. Significant differences in characteristics and dietary intakes in the categories of total fat intake and SFA using joint categories of higher (≥ median) versus lower (< median) intakes were evaluated using one-way analysis of variance for continuous variables and values were reported as Mean (SD). Chi-square test was used to detect any significant differences in the distribution of participants across quartile categories with regard to qualitative variables.
Odds Ratio (ORs) and their 95 % confidence intervals was estimated for the MetS and its components, according to both unsaturated fatty acids (MUFA and PUFA) and SFA using joint categories of higher median intake versus lower median intakes [22 (link)], by multivariable logistic regression models. In all multivariate models, subjects with lower median intakes of both unsaturated fatty acids (MUFA and PUFA) and SFA were considered as the reference.
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2

Statistical Analysis of Research Data

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Statistical analysis was performed using standardized statistical software (Statistical Package for Social Science; version 19.0; SPSS, Inc., Chicago, IL). Non-normally distributed data were expressed as median (25th, 75th interquartile range). Enumeration data were expressed as frequency, rate, or composition ratio, and tested with the chi-squared test. P values of < 0.05 were considered statistically significant.
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3

Evaluation of Healthy Lifestyle Intervention

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Data were checked, cleaned, coded, and entered into Epidata 3.1 version and then it was exported to Statistical Package for Social Science (version 24) for further analysis. Conceptual analyses were conducted with a different model to evaluate the effectiveness. Using Statistical Package for Social Science version 24, the baseline difference in findings from statistics values between the 2 groups and the mean difference in implementation of healthy lifestyle educational intervention between the intervention and control groups was examined. The correlation between variables was analyzed considering team leadership as an independent variable.
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4

Forensic Substance Analysis in Suicide and Accidents

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We used the Statistical Package for Social Science (version 23.0; SPSS Inc., Armonk, NY, USA). We present estimates of post-mortem forensic findings by the manner of death (suicide, accidental, undetermined and natural death) in the Norwegian and Swedish datasets as numbers and percentages. Proportions of detected substances were compared between suicides and accidents in each country using chi-squared tests. Chi-squared tests were also used to compare the frequency of psychoactive substances in suicides/accidents by poisoning between the Norwegian and Swedish datasets. Two-sided P-values < 0.05 were regarded as statistically significant.
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5

Statistical Analysis for Social Science

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Statistical analysis was performed with Statistical Package for Social Science (version 12.0; SPSS Inc., Chicago, IL, USA). Quantitative variables are presented as mean ± standard deviation. Student’s t-test was used to analyze continuous, normally distributed variables. P-values <0.05 were considered statistically significant.
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6

Statistical Analysis of Quantitative and Qualitative Data

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The data were analyzed using Statistical Package for Social Science (version 20, SPSS Inc., Chicago, IL). All quantitative variables were presented as mean ± standard deviation, whereas qualitative data were represented as a number of frequencies or percentages. Comparisons among three groups were done by Chi-square test for qualitative variables and ANOVA test for quantitative variables. A p value of less than 0.05 was considered statistically significant.
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7

Effect of NA Concentrations on Cellular Characteristics

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Data was analyzed with the Statistical Package for Social Science (Version 17.0, SPSS, Chicago, IL, USA). A one-way analysis of variance (ANOVA) was used to evaluate the effect of NA concentrations on the number of adhered cells, hydrophobicity and gene expression. The post hoc comparisons were carried out with Bonferroni correction for multiple testing. p < 0.05 was considered as statistically significant.
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8

Statistical Analysis of Quantitative and Qualitative Data

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The collected data were coded, entered, presented, and analyzed by computer using Statistical Package for Social Science (version 22, SPSS Inc., Chicago, IL) software program. Quantitative variables were expressed as the mean ± standard deviation (SD) while the qualitative variables were expressed as a number and percentage. Independent t-test was used to compare the difference between two means. Chi-square test was used to detect the relation between different qualitative variables. The results were considered statistically significant when the significant probability (P value) of < 0.05.
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9

Statistical Analysis of Measurement Repeatability

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The data were analyzed using the Statistical Package for Social Science (version 16.0; SPSS, Chicago, IL, USA). Standard descriptive methods were used to summarize the data and Lin`s concordance correlation coefficient and the Bland and Altman scatter diagrams were used to test for repeatability.
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